Machine Learning Engineer, Vulcan

About the role

About the role

We are looking for a talented Machine Learning Engineer to join our Product Core Engineering team. You will be responsible for building and optimizing machine learning workflows that directly power our AI-driven products. This role focuses on the full lifecycle of model development — from training and fine-tuning to deployment and monitoring — ensuring robust and efficient ML systems at scale.

Why Join Us?

- Product Impact: Your work will be directly embedded in our core AI products, shaping user experience and product capabilities.

- Engineering Excellence: Be part of a team that values high-quality engineering, reproducibility, and scalability.

- Innovation: Opportunity to experiment with cutting-edge ML and GenAI technologies in production settings.

- Collaboration: Work alongside backend, platform, and product teams in a highly collaborative environment.

- Competitive Package: Receive attractive compensation and benefits aligned with your skills and performance.

Key Responsibilities

- Model Development: Design and implement training processes for machine learning classifiers and generative models.

- Fine-tuning & Prompting: Adapt pre-trained models to specific product needs through fine-tuning, prompt engineering, and parameter optimization.

- Hyperparameter Management: Configure and tune hyperparameters to balance accuracy, robustness, and performance.

- Pipeline Engineering: Build scalable training and evaluation pipelines to support continuous experimentation.

- Integration: Collaborate with backend and product engineers to deploy models into production systems.

- Monitoring & Maintenance: Establish monitoring metrics and retraining strategies to maintain model performance in dynamic environments.

Qualifications

- Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or a related field.

- Proven experience in training classifiers and fine-tuning transformer-based models.

- Strong understanding of tokenization techniques, embedding models, and vector representations.

- Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or Hugging Face Transformers.

- Familiarity with hyperparameter tuning and model configuration best practices.

- Experience working with large-scale datasets and building reproducible ML pipelines.

- Fluent in Mandarin; proficiency in English is an advantage.

Desired Skills

- Knowledge of cloud platforms (AWS, Azure, GCP) and containerized deployment (Docker, Kubernetes).

- Experience with LLM prompting and fine-tuning is a plus.

- Basic understanding of data engineering practices (ETL, data validation, feature engineering).

- Exposure to AI safety, security, or bias mitigation techniques is an advantage.